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Myles Allen writes

Myles Allen has asked me to post this response to the thread in which we discussed his Communicate 2011 lecture.

I do think it is sad for democracy that so much energy in the debate on climate change has been expended on pseudo-debates about the science, leaving no room for public debate about the policy response. In the run-up to Copenhagen, public discussion of effective alternatives to a global cap-and-trade regime (which I would personally view with as much scepticism as most of the readers of this blog) was remarkably absent. It still is, and it always will be as long as the public are kept distracted by a debate over the Medieval Warm Period, which has only ever featured in one of the lines of evidence for human influence on climate (and not, in my view, a particularly strong one). The data we primarily rely upon is the instrumental temperature record, which, as I explained in the talk, emerged from the CRU e-mail affair pretty much unscathed (and I stand by the assertion that one would not have got this impression from media coverage of the issue).

My fear is that by keeping the public focussed on irrelevancies, you are excluding them from the discussion of what we should do about climate change should the decade-to-decade global warming trend observed since the 1970s continue and turn out, as current evidence suggests, to be largely caused by the increase in atmospheric greenhouse gas concentrations. Already, I find people arguing that so much has been invested in the emission cap-and-trade approach that it is too late to consider any alternative. In twenty years time, we may find people arguing that it is too late for any alternative to global geo-engineering, which seems even harder to reconcile with democracy. I believe there are effective alternatives that would represent much less of an intrusion into individual lives and the operation of the economy: for example, — but they aren't going to happen unless we start talking about them.

To be clear, "good for the planet" in the final line of the talk does not, of course, mean "good for us (or our grandchildren)". That is the whole point. I sincerely hope we do not end up in a situation where governments feel justified in taking highly anti-democratic measures to stabilise global temperatures, however effective they might be. I still believe this is a problem we can solve without compromising fundamental democratic principles, but the longer we leave off talking seriously about it, the harder this will be.

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Reader Comments (229)

I note that Richard Betts post (May 29, 2012 at 1:16 PM) references the assessment radiative forcing from a list of sources...
...that can then be used to judge range of uncertainty in this parameter. However, I also note that this list of sources does not include those listed with a 'Very Low' Level of Scientific Understanding (LOSU) in this table...
...which suggests that, if all sources were considered, the true uncertainty may well be very much larger.

I mention this because this is relates exactly the concerns I raised in my previous posts (see this thread on May 26, 2012 at 1:49 PM & May 27, 2012 at 4:48 PM), which suggest that there may well be a significant and common bias in all models if they have excluded a major mechanism (e.g. cosmic rays).

Maybe I've missed some subtle step in the IPCC process that was able to compensate for such uncertainties? If so, I'd be most grateful for Dr Betts or any one else to correct my misunderstanding.

May 29, 2012 at 5:06 PM | Unregistered CommenterDave Salt

It seems to me that there is a significant gap here. What we have is the dead Swede saying all this CO2 in the air is causing a certain amount of heating,, on one side, and an apparent increase in temps concurrent with a CO2 increase (probably, very probably anthropogenic) and a big gap in the middle. Surely there must be something we can measure? Some physical quantity that no-one can dispute, or that anyone can check for himself? It does no good going round and round the same arguments.

Now, those models. They must contain intermediate data about states of the atmosphere or the oceans. The met website says there is checking of model output against real-world data, but I can't recall seeing any results. Richard, can you tell us about that? Not the forecast results but anything about heat flows or IR or something which could be checked? What is the fingerprint of AGW? Or it occurs to me that a black box forcing model wll miss all of that out, in which case that isn't very good methodology, to me.

May 29, 2012 at 9:20 PM | Unregistered CommenterRhoda


Some very thoughtful comments there and much to agree on. I have read one of the papers you cite before, but the other two will make good reading material for me :)

A/ The fluctuations in most temperature records increase on time scales starting at about 30 years and going out to 100,000 years. This is the climate regime.

Indeed. I would go further, and suggest the possibility that the Hurst-Kolmogorov dynamics extend from months out to millions of years (the difficulty in the "plateau" from monthly to 30-year scales in the papers you cite could well be calibration errors between proxy and instrumental data), but further evidence will be helpful in resolving this.

B/ The climate regime scaling behaviour is observed in modern Holocene reconstructions, in ice core reconstructions and in the instrumental record.


C/ The climate regime scaling behaviour is not seen in Holocene reconstructions circa MBH. Reconstructions from that time are outliers from the point of view of their scaling behaviour.

Indeed, and this is an important point missed by many. I might word it slightly differently, but the point is the same: MBH98 and other reconstructions give different statistics for natural variability, with non-overlapping confidence intervals, showing that they are unquestionably different. This puts to bed the false claims of many IPCC consensus advocates that the various reconstructions support MBH98; in fact, they show statistically significant disagreement. (Furthermore, the fact that the confidence intervals are non-overlapping shows at least one is wrong; but we should never rule out the possibility that ALL are wrong, and indeed there is good reason to suspect this).

D/ The climate regime scaling behaviour is not generally seen in GCMs. Hence GCMs are currently unable to simulate climate changes, even though they are very good at simulating weather. It is not known whether this is because of unaccounted external forcings or unaccounted internal natural variability. Either way, it is unaccounted.

Absolutely, 100%. This is why models should not be used as null hypotheses in the way Myles Allen et al propose. You will not get a straight answer on this from a climate scientist. I have been asking for 5yrs+ and still not had any kind of a scientific justification for the type of test they apply.

The Itia group led by Prof. Koutsoyiannis are also working on a physical basis for this behaviour, which is still in development, based on maximisation of entropy and statistical thermodynamics; the reference is:

Koutsoyiannis, D., Hurst-Kolmogorov dynamics as a result of extremal entropy production, Physica A: Statistical Mechanics and its Applications, 390 (8), 1424–1432, 2011.

Preprint available on the Itia website (here)

May 29, 2012 at 10:57 PM | Unregistered CommenterSpence_UK


Thanks very much for your comments and for the reference! I guess I must be experiencing for myself your point that I will "not get a straight answer on this from a climate scientist" (not even one as likeable and persuasive as Richard). It's very disappointing. The consolation, I guess, is that this is such a fascinating area of physics.

May 30, 2012 at 8:23 PM | Registered CommenterPhilip Richens

Let me try to sum up what I’ve got out of the past few days, since some people seem to be still following this thread. I suggested in the Communicate2011 talk that the 0.02K revision to the HadCRUT temperature record was the only change to any published dataset used in the detection and attribution of human influence on climate to have resulted from the UEA e-mail affair, and that this was not generally appreciated by the public. I was using this as an example of how things have gone wrong in communicating climate science: this was not a talk about “climategate” per se.

On whether the “only change” statement was strictly correct, Steve McIntyre has pointed out that the e-mails raised new questions with the treatment of paleoclimate records, Judith Curry has observed that these records are needed to check our estimates of internal climate variability and Ross McKitrick has argued that some of the e-mails showed an improper dismissal of his paper on the correspondence between patterns of warming in the instrumental record and patterns of economic development. Many other points have been raised, but I would like to address these three.

I accept Steve’s point that paleoclimate reconstructions continue to evolve and fresh sources of uncertainty continue to emerge, although my impression is that they were evolving anyway before release of the e-mails and would have continued to do so regardless. This continued uncertainty is a key factor making it difficult for scientists like myself, outside the dendroclimatology community, to make use of tree-ring based data. In trying to cope with multiple blog threads simultaneously, I probably went too far in disparaging tree-ring data, for which I apologize to any dendroclimatologists who might be reading these threads. I do believe efforts to reconstruct pre-instrumental climate represent an interesting and worthwhile challenge: my point was simply that many people seem to think it is the main point of climate research, which it is not.

In response to Judith Curry’s point about the need for proxy reconstructions to test model-simulated internal variability, again, this is a question of “it would be nice if only we were able to do so.” In my personal view, the uncertainties and potential biases in the spectrum of variability that must arise from the process of stitching together multiple tree-ring records (many of which have to be individually detrended), and the fact that we know GCM-simulated variability is deficient on the small scales that the trees are responding to, make it difficult to use proxy records to falsify GCM-simulated large-scale variability. If a GCM disagrees with a paleo-record, do we reject the GCM’s internal variability, the forcing data used to drive the GCM, or the paleo-record itself? We do have observations of variability on sub-century timescales through the instrumental record and new products like the 20th century reanalyses: I think, in the short term at least, these potentially provide more information on internal variability than the millennial reconstructions.

Since the key question for attribution is the origins of the surface warming over the past 30 years, that being the only period for which we have direct observations of forcing, it is the spectrum of internal variability on 20-100 year timescales that is essential. Variability on longer timescales is less important for attribution of causes for the current warming trend. I stress this statement applies to surface temperature. Sea level responds on different timescales, making attribution correspondingly harder.

In response to Ross McKitrick’s point (apologies for being slow on this one), I suspect what Phil Jones was referring to in the “no need to calculate a p-value” remark (although you should really ask him) was the danger of over-interpreting chance covariation. The only p-values that mean anything are those that derive from physically-based hypotheses. It is all too easy to find a high p-value from a chance correlation (sunspots and number of Republicans in the US Senate is the classic example). I wasn’t involved at all in that IPCC chapter, but I would be inclined to agree with their assessment that what you were seeing in that paper was an example of such an acausal covariation, for which the p-value of a pattern-correlation is indeed meaningless.

Then there is the much more general point, raised by Lucia, Rhoda and many others, that my talk was misleading, because “climategate” was not about the data at all, but rather about scientific process and the probity of climate scientists. As Mike Hulme observed, “climategate” meant different things to different people: for me, the implications for the instrumental record were all-important, which is why I was castigating the British press for paying far less attention to the fact that the instrumental record got an almost (in deference to Ross) completely clean bill of health than it paid to the initial allegations. There was an interesting side-thread on why the HadCRUT got dragged into this in the first place, to which I don’t have much to add apart from reassuring everyone that I don’t blame the bloggers for this confusion.

Many people have asserted that the main impact of “climategate” is that we can no longer say “trust me, I’m a climate scientist” until we all come out and condemn CRU, Muir-Russell, Oxburgh, etc. “Trust me, I’m a climate scientist” is not a phrase I have ever used, and I hope I never will. I teach a 12-lecture course to our 3rd year physics students (open to the public if anyone is interested) that starts from the premise “Don’t trust climate scientists” – the point being that, as physicists, they should be able to understand the problem for themselves, and not be expected to take the IPCC’s word for it.

The only basis of trust in science is the reproducibility of results. This is why availability of data and model source code is so important: I have always supported open data, although I have also consistently said that I don’t think Freedom of Information requests are the right way to enforce it. Journal editors can and should enforce a simple “disclose or retract” policy if a result is challenged, and almost all of them do: if any don’t, then the solution is to name and shame them, not set up a parallel enforcement system. I also think it is always better to reproduce results from equations (and, where possible, independent models and observations) rather than “auditing” computer code.

Finally, on the “bad for democracy” remark that upset a lot of people. I don’t want to suppress discussion of the Medieval Warm Period, but everything has an opportunity cost. Time spent arguing over paleoclimate research is time not spent on, for example, the merits of the two degree “goal” agreed in Copenhagen and Cancun, with remarkably little scientific justification. Yet whether we aim to limit anthropogenic warming to two, three or four degrees has far bigger implications for climate policy than the existence or otherwise of the Medieval Warm Period. Why is this not a hot topic in the blogosphere?

Ironically, this whole discussion started from a throw-away post by Paul Matthews in a discussion of a lecture I recently gave on whether it would be possible to frame an effective climate mitigation policy that did not extend the reach of the State in the way that cap-and-trade, carbon rationing or geo-engineering clearly will. Paul has apologized, which is much appreciated, but the damage may be done, Paul. If the European Commission decide to impose carbon rationing in 2020 after another record-breaking warm decade, because we spent this past week discussing Myles Allen’s interpretation of climategate (not to mention his, admittedly poor, choice of shirts) rather than coming up with a less intrusive policy alternative, your grandchildren shall know the reason why.

Apologies for cross-posting on various threads.

May 31, 2012 at 12:44 PM | Unregistered CommenterMyles Allen

Myles Allen:
May I respectfully suggest that you get a grip. Making minimalist arguments for urgent action is not enough. There are too many holes in your case for action. There are too many negative consequences of mitigation; the emotive appeal to grandchildren works equally well for those consequences. If you want genuinely to reach out to people, then notice that scientific questions asked on this thread relate directly to your statement that “it is the spectrum of internal variability on 20-100 year timescales that is essential”. One possible starting point might be to address these questions.

May 31, 2012 at 6:34 PM | Registered CommenterPhilip Richens

May 29, 2012 at 3:51 PM | philiprichens

Hi Philip

Sorry for the delayed response, I got distracted over on Unthreaded!

1/ Reconstructed natural variability on time-scales > 30 years has similar statistical properties to 20th century changes

Is that really true? I don't think the papers you showed earlier actually show that. Do any other papers show that? I don't think so - but happy to stand corrected.

2/ GCMs are unable to simulate this variability.

The papers you showed do suggest that GCMs (at least, the ones they looked at) tend to underestimate variability at longer timescales, but I could not see this quantified in the context of 20th Century warming in those papers.

There is no actual demonstration in those papers that observed 20th Century changes can be explained purely (or even predominantly) in terms of internal variability as inferred from palaeoclimate reconstructions. So this is not "proof" that recent warming is variability, it is just a word of caution about using the models to estimate variability.

I completely agree that the GCMs should not be assumed to be perfect in their representation of internal variability on all timescales, and indeed that assumption is not made in AR4 either. Please read the attribution chapter in detail!

Have a good weekend!



Jun 1, 2012 at 3:42 PM | Registered CommenterRichard Betts

Hi Richard,

Thanks for the further reply, that certainly helps to restore my confidence.

With regard to to my statement that "natural variability on time-scales > 30 years has similar statistical properties to 20th century changes" you asked,

Is that really true? I don't think the papers you showed earlier actually show that.

With reference to Low frequency weather and the emergence of the Climate, please first look at figure 5, which shows the low-frequency weather and climate regimes for the instrumental record.

Figure 9 shows the comparison of scaling between the instrumental and multi-proxy records. This shows that the instrumental and post-2003 multi-proxy scaling exponents are the same, whereas the pre-2003 exponent is different from both. The offset between red and purple is the discrepancy left unexplained in section 4.4.

Ice-core reconstructions show similar scaling exponents to the instrumental and post-2003 multi-proxy. See table 3, or figure 10 for a comparison with the instrumental and multi-proxy records.

The fact that modern multi-proxy reconstructions do show the same climate scaling exponent as the other records, suggests that they are far more useful than Myles has suggested. There are many caveats in the analysis, I agree. Nonetheless, the authors seem very confident about the veracity of their basic conclusions.

Although this basic overall three-regime scaling picture is 25 years old, much has changed to make it more convincing. Obviously, an important element is the improvement in the quantity and quality of the data, but we have also benefited from advances in nonlinear dynamics as well as in data analysis techniques. In combination, these advances make the three-regime scaling model a seductive framework for understanding atmospheric variability over huge ranges of space-time scales. It allows us to finally clarify the distinction between weather, the straightforward extension of the weather dynamics - without new elements - to low frequency weather and finally to the climate regime. It allows for objective definitions of the weather (scales < tau_w), climate states (averages up to tau_c), and hence of climate change (scales > tau_c). This new understanding of atmospheric variability is essential in evaluating the realism of both atmospheric and climate models. In particular - since without special external forcing GCM’s only model low frequency weather - the question is posed as to what types of external forcing are required so that the GCM variability makes a transition to the climate regime with realistic scaling exponents and at a realistic time scales.

As I mentioned before, there are numerous references in this paper, and in their earlier papers as well. See here for links to their other papers and articles. You will also see that these authors have an upcoming Cambridge Press book on this topic.

The papers you showed do suggest that GCMs (at least, the ones they looked at) tend to underestimate variability at longer timescales, but I could not see this quantified in the context of 20th Century warming in those papers.

Thank you for accepting that the GCMs used in these studies do underestimate variability. One of my original questions to you was intended to double check this point. Do the MO models also underestimate the variability at longer timescales? What climate scaling exponent do they exhibit?

I'm not quite sure what you’re asking for regarding "in the context of 20th century warming". The GCM variability tests are not attempting to reproduce 20th century warming, but to find out how the models scale. The observed scaling laws for shorter timescales (weather and low frequency weather) are reproduced very well by the GCMs, but they do not generally reproduce the climate regime i.e. changes over decadal and longer time-scales.

There is no actual demonstration in those papers that observed 20th Century changes can be explained purely (or even predominantly) in terms of internal variability as inferred from palaeoclimate reconstructions. So this is not "proof" that recent warming is variability, it is just a word of caution about using the models to estimate variability.

I agree with you that this is not "proof", but also think that the findings are more than "just a word of caution about using the models to estimate variability". The papers demonstrate that:-

1/ The climate time-scale variability (as encoded by the scaling laws) of the instrumental record and modern Holocene reconstructions are comparable.

2/ GCMs (or at least the ones studied by the authors) do not tend to reproduce this climate time-scale variability.

Myles was very clear that his main argument for attribution was the comparison of the instrumental record with model outputs. In the light of the McGill findings, this becomes very unconvincing.

Researching this, I've encountered several other reasons for scepticism about model outputs on decadal and longer time-scales, as I mentioned to you on the "Evidence, confidence and uncertainties" thread. One important point, is that by tweaking parametrizations model outputs will vary over an entire order of magnitude. I know you have rightly pointed out that this range can be constrained using comparison with observation. However I think this rather misses the point, which is that the models cannot currently be further constrained by the known physical processes. Hence the best physical understanding (at least as encoded into the GCMs) is not able to simulate the climate to better than an order of magnitude.

A final comment (for this round anyway!). I think the McGill research highlights a very basic difference in perspective between the two camps. One viewpoint holds that Holocene climate is basically flat with a superimposed white noise caused by weather processes. This viewpoint is epitomised by the hockey stick shape. If this is how you see things, that it is obviously absurd to imagine that 20th C changes could be anything except man-made. The other viewpoint holds that Holocene climate has variability on all scales. If you think this is the case (and it is this viewpoint that is confirmed by the McGill findings), then it is obviously absurd to imagine that 20th C changes can be attributed by examining just the 20th C record.

You have a good weekend too, but take your brolly!

PS: Regarding chapter 9, please bear with me, I will read through it again with a particular eye to this current discussion. If there was anything in particular you wanted me to focus on, please point it out.

Jun 2, 2012 at 6:52 AM | Registered CommenterPhilip Richens

Isn't nature wonderful?

Somehow a system has evolved which, (at least over the past 150 years,) has maintained a temperature stable to 0.24%. Over longer periods variations have been less than 5%. Marvellous!


Jun 2, 2012 at 12:59 PM | Unregistered CommenterDavid A. Evans

From philiprichens:

1/ Reconstructed natural variability on time-scales > 30 years has similar statistical properties to 20th century changes

From Richard Betts:
Is that really true? I don't think the papers you showed earlier actually show that. Do any other papers show that? I don't think so - but happy to stand corrected.

Many papers have looked for the presence of long-term persistence in climate time series and confirmed the presence of it. This includes papers both from "sceptics" and from more pro-IPCC advocates. Nobody, to my knowledge, has falsified this proposition yet. This is a point Richard Tol made at Judith Curry's place on a discussion of the topic.

It would take a while for me to dig up all the references, but examples would include the paper by Rybski et al (von Storch was a co-author of this one), responses by Koutsoyiannis, Halley, etc. etc.

A useful summary can be found here on pp 28-32 (click through to presentation). This isn't a paper but a presentation to a scientific conference, but takes well known instrumental and proxy records and estimates statistics from them. It confirms the presence of long-term persistence (Hurst-Kolmogorov dynamics) and demonstrates the consistent Hurst exponent across a wide range of timescales, from months out to thousands of years.

There is no actual demonstration in those papers that observed 20th Century changes can be explained purely (or even predominantly) in terms of internal variability as inferred from palaeoclimate reconstructions. So this is not "proof" that recent warming is variability, it is just a word of caution about using the models to estimate variability.

Firstly, we need to bound the problem. AGW is a claimed effect so as per tradition in science the burden of evidence is placed on those claiming an effect. Secondly, I think we all agree that natural variability exists and has the potential to confound climate measurements. So unless you are claiming that natural variability doesn't exist, it must form part of the null hypothesis of "no measurable effect" when we perform our tests to see if AGW is the explanation for the 20th century trends.

Of course, then we can simply refer to the likes of Cohn and Lins 2005 in demonstrating the (surprisingly large) effect that long term persistence can have on p-values. Coupled with the observation that every scientist that has looked at it (to my knowledge) has confirmed the presence of long-term persistence in climatic time series, I would not accept any detection+attribution test that has not at least assessed the sensitivity of that test to long term persistence in natural variability.

It's notable that once you accept that long-term persistence precludes the instrumental record alone being used to provide evidence for AGW, those supporting the theory (e.g. Rybski et al) rely on the proxy records instead to provide supporting evidence. However, as Koutsoyiannis and Halley note, the proxy records themselves are inconsistent (i.e., statistical parameters estimated from the different proxy records are different with non-overlapping confidence intervals), which means they cannot be relied on either.

Some refs that I could locate quickly (there are more recent ones but I'd need to do some work to locate them)

Cohn, T. A., and H. F. Lins (2005), Nature's style: Naturally trendy, Geophys. Res. Lett., 32(23), L23402, doi:10.1029/2005GL024476.

Rybski, D., A. Bunde, S. Havlin, and H. von Storch (2006), Long-term persistence in climate and the detection problem, Geophys. Res. Lett., 33, L06718, doi:10.1029/2005GL025591.

Koutsoyiannis, D., and A. Montanari, Statistical analysis of hydroclimatic time series: Uncertainty and insights, Water Resources Research, 43 (5), W05429, 2007.

Jun 3, 2012 at 4:32 PM | Unregistered CommenterSpence_UK


First of all, thank you very much for encouraging me to reread AR4 chapter 9. From the point of view of its treatment of internal variability, my impression of it is much in line with my earlier comments.

1/ The chapter tends to view internal variability as short-term white noise, tailing away at decadal and longer time-scales, as occurs within GCM simulations.
2/ The arguments in favour of a small contribution from natural variability to recent warming, rely heavily on those same GCM simulations, even though the GCMs do not simulate the observed climate-scale variability.

Here are snips from the chapter that illustrate these two points (page numbers from pdf):-

An identified change is ‘detected’ in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small.

Such patterns, or ‘fingerprints’, are usually derived from changes simulated by a climate model in response to forcing.

... when fingerprints from Atmosphere-Ocean General Circulation Models (AOGCMs) are used, averaging over an ensemble of coupled model simulations helps separate the model’s response to forcing from its simulated internal variability.

Thus, internal climate variability is usually estimated from long control simulations from coupled climate models.

Using results from a range of detection studies of the instrumental record, which was assessed using fingerprints and estimates of internal climate variability from several climate models, it was found that the warming over the 20th century was ‘very unlikely to be due to internal variability alone as estimated by current models’.

A number of uncertainties remained at the time of the TAR. For example, large uncertainties remained in estimates of internal climate variability. However, even substantially inflated (doubled or more) estimates of model-simulated internal variance were found unlikely to be large enough to nullify the detection of an anthropogenic influence on climate.

The variability that remains in proxy reconstructions after estimates of the responses to external forcing have been removed is broadly consistent with AOGCM-simulated internal variability...

The interannual variability in the individual simulations that is evident in Figure 9.5 suggests that current models generally simulate large-scale natural internal variability quite well...

A further source of uncertainty derives from the estimates of internal variability that are required for all detection analyses. These estimates are generally model-based because of difficulties in obtaining reliable internal variability estimates from the observational record on the spatial and temporal scales considered in detection studies.

Climate models provide a suitable tool to study the various influences on the Earth’s climate.

The models fail to reproduce the observed warming when run using only natural factors.

Variations in the Earth’s climate over time are caused by natural internal processes, such as El Niño, as well as changes in external influences.

The role of natural internal processes can be estimated by studying observed variations in climate and by running climate models without changing any of the external factors that affect climate.

Although natural internal climate processes, such as El Niño, can cause variations in global mean temperature for relatively short periods ...

Numerous experiments have been conducted using climate models to determine the likely causes of the 20th-century climate change. These experiments indicate that models cannot reproduce the rapid warming observed in recent decades when they only take into account variations in solar output and volcanic activity.

Comparison with observations shows that the models used in these studies appear to have an adequate representation of internal variability on the decadal to inter-decadal time scales important for detection (Figure 9.7).

Gent and Danabasoglu (2004) show that the observed trend cannot be explained by natural internal variability as simulated by a long control run of the Community Climate System Model (CCSM2).

They report that whereas the observed change is not consistent with internal variability and the response to natural external forcing as simulated by two climate models (PCM and HadCM3) ...

The observed warming is highly significant relative to estimates of internal climate variability which, while obtained from models, are consistent with estimates obtained from both instrumental data and palaeoclimate reconstructions.

Observed change is very large relative to climate-model simulated internal variability. Surface temperature variability simulated by models is consistent with variability estimated from instrumental and palaeorecords.

Two recent comments on this blog by climate scientists also confirm the idea that the IPCC story is based on the view of internal variability as short-term white noise.

Myles Allen, just a few comments above this one, mentioned that the 2000s is the warmest decade in the instrumental record. However, from the point of view of attribution this observation only makes sense if you think that natural variability is short term white noise. It is meaningless if you think that there is long term variability (as suggested by the multi-proxy and ice core reconstructions, as well as by the instrumental record).

Ed Hawkins, a few weeks ago, dismissed Pielke's criticism of his "famous paper". But the suggestion in his paper that the GHG trend will quickly emerge from the background of natural variability also only make sense if you think that natural variability means short term white noise. If you think it means long term variability, then you will need to wait a very long time before you can say with confidence that an emerging trend has been identified.

I also notice that Koutsoyiannis makes the very same criticism about the white noise view of internal variability in slide 8 of the presentation referenced by Spence_UK.

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